Navigating my Way in, through, and out of PVE-Centered Instruction: Autoethnographic Reflections of Researching and Teaching PVE in CEGEP Literature Classrooms
Bibliographic record
Abstract
As a college instructor who has previously researched, developed, and implemented Preventing Violent Extremism (PVE) curricula for my literature classes, I have identified several benefits, risks, and needs associated with teaching PVE in higher education. In this dissertation, I use critical autoethnography to elucidate my experience as a PVE researcher-practitioner from 2013 to 2016 at a CEGEP in the province of Quebec in Canada. I have done do so to improve my own practice as an instructor, to shed light on issues that may present barriers to effective PVE instruction, and to work toward socially just education. Autoethnography has been a useful method for understanding my experience as a PVE researcher-practitioner. It offers valuable insights into how the PVE-centered course I designed and taught both aligned with and diverged from recommendations in the literature. I found the experience to be paradoxically hopeful and despairing. On the one hand, the benefits of teaching PVE are promising, as they include fostering civic engagement and serving as a protective factor against radicalization. On the other hand, my research points to a number of potential drawbacks that, in my case, appeared to outweigh these benefits. These drawbacks include the potential risks that PVE poses to the students I teach and the negative experience I encountered while simultaneously researching PVE, designing timely and carefully designed PVE curricula, dealing with the emotionally charged content, and teaching those curricula. This resulted in a demanding workload, a heavy emotional and psychological toll, and a decline in my health and morale. These experiences prompted me to rethink and ultimately reconceptualize teaching my stand-alone PVE-centric course in favour of courses that focus primarily on teaching critical reading and critical thinking skills, since critical thinking can be beneficial in PVE and can bolster civic engagement—skills necessary for preventing violence in all forms. Additionally, I have found that balancing content that presents narratives of oppression with content that presents positive counter-narratives to be helpful in building resilience and instilling hope, motivation, and improved well-being.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".